Compared to the depth completion methods, we obtain state-of-the-art performance while using clearly less model parameters as shown in Figure REF  a).
The best performing baselines, NLSPN [1]}, DepthNormal [2]}, GuideNet [3]} use \(2.9\) , \(3.4\) , and \(7.3\)  times more parameters compared to our method, respectively. Instead of using the explicit 3D points, the multi-view stereo [4]}, structure-from-motion [5]}, and SLAM [6]} methods utilise multiple RGB images with camera poses. The results in Table REF  indicate that the proposed model outperforms also these methods using only a fraction of the model parameters.
